Assessing the State-of-Practice of Model-Based Engineering in the Embedded Systems Domain

Liebel, Grischa; Marko, Nadja Christiane; Tichy, Matthias; Leitner, Andrea; Hansson, Jörgen

doi:10.1007/978-3-319-11653-2_11

Cited by 46 publications

(58 citation statements)

References 14 publications

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“…The additional challenge here compared to the above-discussed challenge of round-trip engineering is that these on-site technicians or skilled workers are typically not trained to use model-driven engineering approaches and the acceptance of modeling languages is low. Furthermore, the additional licenses of the modeling software required for on-site changes might be prohibitive for supplier staff and even more applicable for customer staff Finally, three recent surveys of practice in model-driven approaches for embedded systems and usability of model-driven engineering notations in embedded systems and, specifically, aPS (Hutchinson et al, 2011;Liebel et al, 2014;Vogel-Heuser, 2014e) show that tool integration and tool usability as well as high effort to reap benefits are big challenges in employing model-driven engineering in industry. Education is an issue related to this.…”

Section: Challengesmentioning

confidence: 98%

See 1 more Smart Citation

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

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253

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a b s t r a c tCoping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

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Section: Challengesmentioning

confidence: 98%

“…While model-driven engineering shows an increase in effectiveness and quality and is used in the embedded software industry (Liebel et al, 2014) by exploiting domain knowledge, it also introduces general challenges and challenges specific to aPS.…”

Section: Challengesmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

Self Cite

253

View full text Add to dashboard Cite

show abstract

“…The SysML standard [19] defines a notation, not the way of using it. SysML therefore needs to be associated with a methodology.…”

Section: Methodsmentioning

confidence: 99%

General architecture for data analysis in industry 4.0 using SysML and model based system engineering

Arantes

Bonnard

Mattei

et al. 2018

2018 Annual IEEE International Systems Conference (SysCon)

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Abstract-The industrial manufacturing sector has substantially evolved over the past decade, with the collect and analyze of sensors and apparatus data. The manufacturing processes "digitalization" has resulted in huge data management concerns, as manufacturing companies need to aggregate value for productivity improvement. The Industry 4.0 concept involves many technologies in order to challenge this issue. The paper presents an architecture framework to allow data analysis in different factories in the Industry 4.0 context. A MBSE approach is proposed and the use of SysML is assessed in that context.

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“…Figure 1 (adopted from [4]) depicts these concepts. Although there have been a few prior surveys related to modeling in the embedded software industry (e.g., [5][6][7]), they have either focused on only one aspect of modeling, (e.g., the use of Unified Modeling Language (UML)) or the use of formal models, or modeling in regional contexts (e.g., in Brazil or in Greece). There are also surveys, whose participants were involved with model-based techniques on a single sub-domain of embedded systems (i.e., the automotive industry [5]).…”

Section: A Terminology: Mbe Versus Mde and Mddmentioning

confidence: 99%

Cross-factor analysis of software modeling practices versus practitioner demographics in the embedded software industry

Akdur

Garousi

Demirörs

2017

2017 6th Mediterranean Conference on Embedded Computing (MECO)

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Abstract-Software-intensive embedded systems have evolved to be essential tools of our lives. To cope with growing complexities in embedded software industry, software modeling and modeldriven engineering (MDE) have become popular for design, development and testing of these systems. However, the usage of models in embedded software industry and the relevant practices usually vary since challenges, requirements and purposes differ among systems as well as among sectors. To understand the stateof-the-practice of software modeling in embedded systems industry, we designed and conducted a world-wide survey, which accepted 627 responses from 27 different countries. The goal of this paper is to present our work built on this survey to better understand and characterize the cross-factor analysis of software modeling versus practitioner demographics, e.g., degree of using software modeling versus target sectors of the products.

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Assessing the State-of-Practice of Model-Based Engineering in the Embedded Systems Domain

Cited by 46 publications

References 14 publications

Evolution of software in automated production systems: Challenges and research directions

Evolution of software in automated production systems: Challenges and research directions

General architecture for data analysis in industry 4.0 using SysML and model based system engineering

Cross-factor analysis of software modeling practices versus practitioner demographics in the embedded software industry

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